@article{almchr00,
    author = {Almgren, R. F. and Chriss, N.},
    citeulike-article-id = {7797147},
    citeulike-linkout-0 = {http://www.thejournalofrisk.com/public/showPage.html?page=1519},
    journal = {Journal of Risk},
    keywords = {optimal-trading, portfolio\_transactions},
    number = {2},
    pages = {5--39},
    posted-at = {2010-09-08 09:46:31},
    priority = {0},
    title = {Optimal execution of portfolio transactions},
    url = {http://www.thejournalofrisk.com/public/showPage.html?page=1519},
    volume = {3},
    year = {2000}
}

@article{almgren03,
    abstract = {Optimal trading strategies are determined for liquidation of a large single-asset portfolio to minimize a combination of volatility risk and market impact costs. The market impact cost per share is taken to be a power law function of the trading rate, with an arbitrary positive exponent. This includes, for example, the square root law that has been proposed based on market microstructure theory. In analogy to the linear model, a characteristic time for optimal trading is defined, which now depends on the initial portfolio size and decreases as execution proceeds. A model is also considered in which uncertainty of the realized price is increased by demanding rapid execution; it is shown that optimal trajectories are described by a critical portfolio size above which this effect is dominant and below which it may be neglected.},
    author = {Almgren, Robert F.},
    citeulike-article-id = {9007046},
    citeulike-linkout-0 = {http://dx.doi.org/10.1080/135048602100056},
    doi = {10.1080/135048602100056},
    journal = {Applied Mathematical Finance},
    keywords = {market-impact, optimal-trading},
    number = {1},
    pages = {1--18},
    posted-at = {2011-03-17 08:49:55},
    priority = {2},
    publisher = {Routledge},
    title = {Optimal execution with nonlinear impact functions and trading-enhanced risk},
    url = {http://dx.doi.org/10.1080/135048602100056},
    volume = {10},
    year = {2003}
}

@article{ga10,
    abstract = {Starting from a no-dynamic-arbitrage principle that imposes that trading costs should be non-negative on average and a simple model for the evolution of market prices, we demonstrate a relationship between the shape of the market impact function describing the average response of the market price to traded quantity and the function that describes the decay of market impact. In particular, we show that the widely assumed exponential decay of market impact is compatible only with linear market impact. We derive various inequalities relating the typical shape of the observed market impact function to the decay of market impact, noting that, empirically, these inequalities are typically close to being equalities.},
    author = {Gatheral, Jim},
    citeulike-article-id = {7799918},
    citeulike-linkout-0 = {http://dx.doi.org/10.1080/14697680903373692},
    doi = {10.1080/14697680903373692},
    journal = {Quantitative Finance},
    keywords = {market-impact, optimal-trading},
    number = {7},
    pages = {749--759},
    posted-at = {2010-09-08 10:08:59},
    priority = {0},
    publisher = {Routledge},
    title = {No-dynamic-arbitrage and market impact},
    url = {http://dx.doi.org/10.1080/14697680903373692},
    volume = {10},
    year = {2010}
}

@article{lehalle08,
    abstract = {The progressive availability of automated access to exchanges and the continuously increasing capabilities of electronics (capture, storage and processing of information) allows to apply rigorous methods to optimise intra day trading. Aside from the robots dedicated to place orders and blindly slice, synchronise and spray them on fragmented markets (like cash and carry robots or first generation {MiFID} and Reg {NMS} Smart Order Routers), the combination of high frequency statistics, microstructure theory and stochastic control allows a new generation of auto adaptive algorithms to minimise their implicit trading costs and trading risks [Engle and Ferstenberg, 2006]. Such algorithms take into account the closed loop they establish with the markets. They rely on quantitative measurements of their performances in terms of returns, risks and views on how to mix them [Bertsimas and Lo, 1998]. They also use models of their interaction with the market microstructure. Their inputs are fine estimates of intra day markets invariants and seasonalities that have to be accurate in a coherent way with the used models. To be short, such trading algorithms can be formalised inside a stochastic control framework [Almgren and Chriss, 2000]. Because they not only minimise the trading costs, but also trading risks, they are essential parts of any investment or hedging strategy. This short paper gives critical elements in each of the three underlying fields that are used in quantitative trading optimisation: firstly some explorations around the embedding of trading into a stochastic control framework, then the market impact models that are at the heart of this kind of optimisation, and finally some of the available methods to obtain accurate statistics into an high frequency world.},
    author = {Lehalle, Charle-Albert},
    citeulike-article-id = {3107179},
    journal = {Wilmott Magazine},
    keywords = {intraday-volume, optimal-trading},
    month = nov,
    posted-at = {2010-09-08 10:30:37},
    priority = {0},
    publisher = {Whiley},
    title = {Rigorous optimisation of intra day trading},
    year = {2008}
}

@misc{lehalle08cdf,
    author = {Lehalle, Charles-Albert},
    booktitle = {Atelier Trading \& Micro- structure},
    citeulike-article-id = {11014842},
    institution = {Coll\`{e}ge de France},
    keywords = {market-impact, optimal-trading},
    posted-at = {2012-08-05 11:34:35},
    priority = {2},
    title = {Probl\'{e}matiques dans trading \`{a} haute fr\'{e}quence},
    year = {2008}
}

@misc{bouchaud09,
    abstract = {We define what "Price Impact" means, and how it is measured and modelled in
the recent literature. Although this notion seems to convey the idea of a
forceful and intuitive mechanism, we discuss why things might not be that
simple. Empirical studies show that while the correlation between signed order
flow and price changes is strong, the impact of trades on prices is neither
linear in volume nor permanent. Impact allows private information to be
reflected in prices, but by the same token, random fluctuations in order flow
must also contribute to the volatility of markets.},
    archivePrefix = {arXiv},
    author = {Bouchaud, J. P},
    institution = {Capital Fund Management},
    citeulike-article-id = {9798647},
    citeulike-linkout-0 = {http://arxiv.org/abs/0903.2428},
    citeulike-linkout-1 = {http://arxiv.org/pdf/0903.2428},
    day = {13},
    eprint = {0903.2428},
    keywords = {market-impact},
    month = mar,
    posted-at = {2011-09-23 17:28:18},
    priority = {0},
    title = {Price Impact},
    url = {http://arxiv.org/abs/0903.2428},
    year = {2009}
}

@manual{barra99,
    author = {Torre, Nicolo G. and Ferrari, Mark J.},
    citeulike-article-id = {11016266},
    keywords = {market-impact},
    organization = {BARRA},
    posted-at = {2012-08-05 16:06:27},
    priority = {2},
    publisher = {BARRA Research},
    title = {The Market Impact Model},
    year = {1999}
}